[SelfOrg]5.1 Self-Organization in Autonomous Sensor/Actuator Networks [SelfOrg] Dr.-Ing. Falko...

[SelfOrg] 5.1

Self-Organization in Autonomous Sensor/Actuator Networks

[SelfOrg]

Dr.-Ing. Falko Dressler

Computer Networks and Communication Systems

Department of Computer Sciences

University of Erlangen-Nürnberg

http://www7.informatik.uni-erlangen.de/~dressler/

[email protected]

[SelfOrg] 5.2

Overview

Self-OrganizationIntroduction; system management and control; principles and characteristics; natural self-organization; methods and techniques

Networking Aspects: Ad Hoc and Sensor NetworksAd hoc and sensor networks; self-organization in sensor networks; evaluation criteria; medium access control; ad hoc routing; data-centric networking; clustering

Coordination and Control: Sensor and Actor NetworksSensor and actor networks; communication and coordination; collaboration and task allocation

Self-Organization in Sensor and Actor NetworksBasic methods of self-organization – revisited; evaluation criteria

Bio-inspired NetworkingSwarm intelligence; artificial immune system; cellular signaling pathways

[SelfOrg] 5.3

Bio-inspired Networking

Introduction Swarm intelligence Artificial immune system Cellular signaling pathways

[SelfOrg] 5.4

The term “bio-inspired”

The term bio-inspired has been introduced to demonstrate the strong relation between a particular system or algorithm, which has been proposed to solve a specific problem, and a biological system, which follows a similar procedure or has similar capabilities.

Bio-inspired computing represents a class of algorithms focusing on efficient computing, e.g. for optimization processes and pattern recognition

Bio-inspired systems rely on system architectures for massively distributed and collaborative systems, e.g. for distributed sensing and exploration

Bio-inspired networking is a class of strategies for efficient and scalable networking under uncertain conditions, e.g. for delay tolerant networking

[SelfOrg] 5.5

The design of bio-inspired solutions

Identification of analogies In swarm or molecular biology and IT systems

Understanding Computer modeling of realistic biological behavior

Engineering Model simplification and tuning for IT applications

Identification of analogies between

biology and ICT

Modeling of realistic biological behavior

Model simplification and tuning for ICT

applications

Understanding Engineering

[SelfOrg] 5.6

Bio-inspired research – EAs

Evolutionary algorithms (EAs) Darwin proposed that a population of individuals capable of reproducing and

subjected to genetic variation followed by selection results in new populations of individuals increasingly more fit to their environment

Classes Genetic Algorithms (GAs) Evolution strategies Evolutionary programming Genetic programming Classifier systems

Working principles1. Definition of the search space and of an initial state2. Evaluation of an objective function3. Selection of new candidate states

Examples are simulated annealing and hill-climbing

[SelfOrg] 5.7

Bio-inspired research – ANNs

Artificial neural networks (ANNs) Primary objective of an ANN is to acquire knowledge from the environment

self-learning property

Σ

Input: x1

Input: x2

Input: xn

w1

w2

wn

b

f(u)u Output: y…

Activationfunction

Summingjunction

[SelfOrg] 5.8

Bio-inspired research – others

Swarm intelligence (SI)

Artificial immune system (AIS)

Cellular signaling pathways

[SelfOrg] 5.9

Swarm Intelligence (SI)

“The emergent collective intelligence of groups of simple agents.” (Bonabeau)

• Ants solve complex tasks by simple local means

• Ant productivity is better than the sum of their single activities

• Ants are “grand masters” in search and exploration

[SelfOrg] 5.10

Swarm intelligence

Stigmergy: stigma (sting) + ergon (work) ‘stimulation by work’

Characteristics of stigmergy Indirect agent interaction modification of the

environment Environmental modification serves as external

memory Work can be continued by any individual The same, simple, behavioral rules can create

different designs according to the environmental state

[SelfOrg] 5.11

Swarm intelligence – Collective foraging by ants

(a) Starting from the nest, a random search for the food is performed by foraging ants

(b) Pheromone trails are used to identify the path for returning to the nest

(c) The significant pheromone concentration produced by returning ants marks the shorted path

Nest Food Nest Food

Nest Food

(a)

(c)

(b)

[SelfOrg] 5.12

Ant Colony Optimization (ACO)

Working on a connected graph G = (V,E), the ACO algorithm is able to find a shortest path between any two nodes

Capabilities A colony of ants is employed to build a solution in the graph A probabilistic transition rule is used for determining the next edge of the

graph on which an ant will move; this moving probability is further influenced by a heuristic desirability

The ”routing table” is represented by a pheromone level of each edge indicating the quality of the path

The most important aspect in this algorithm is the transition probability pij for an ant k to move from i to j

[SelfOrg] 5.13

Ant Colony Optimization (ACO)

Jik is the tabu list of not yet visited nodes, i.e. by exploiting Ji

k, an ant k can avoid visiting a node i more than once

ηij is the visibility of j when standing at i, i.e. the inverse of the distance

τij is the pheromone level of edge (i, j), i.e. the learned desirability of choosing node j and currently at node i

α and β are adjustable parameters that control the relative weight of the trail intensity τij and the visibility ηij, respectively

The pheromone decay is implemented as a coefficient ρ with 0 ≤ ρ < 1

τij(t) ← (1 − ρ) × τij(t) + Δτij(t)

otherwise0

if)(

)( ki

Jlilil

ijij

kij

Jjt

t

pki

[SelfOrg] 5.14

AntNet and AntHocNet

Application of ACO for routing

The routing table Tk defines the probabilistic routing policy currently adopted for node k For each destination d and for each neighbor n, Tk stores a probabilistic

value Pnd expressing the quality (desirability) of choosing n as a next hop towards destination d

Forward ants randomly search for ”food” After locating the destination, the agents travel backwards (now called

backward ants) on the same path used for exploration

Reinforcement Positive Pfd ← Pfd + r(1 − Pfd) NegativePnd ← Pnd − rPnd n N∈ k , n ≠ f

)}({

1kneighborn

ndP

[SelfOrg] 5.15

AntHocNet – Performance

[SelfOrg] 5.16

Ant-based task allocation

Combined task allocation and routing ACO used for selection of appropriate nodes to accomplish a task AND for

selecting appropriate routes (similar to AntNet)

Task allocation Routing

[SelfOrg] 5.17

Artificial Immune System (AIS)

“Artificial immune systems are computational systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains”

(de Castro & Timmis)

Why the immune system? Recognition – Ability to recognize pattern that are (slightly) different from

previously known or trained samples, i.e. capability of anomaly detection Robustness – Tolerance against interference and noise Diversity – Applicability in various domains Reinforcement learning – Inherent self-learning capability that is

accelerated if needed through reinforcement techniques Memory – System-inherent memorization of trained pattern Distributed – Autonomous and distributed processing

[SelfOrg] 5.18

Self/Non-Self Recognition

Immune system needs to be able to differentiate between self and non-self cells

Antigenic encounters may result in cell death, therefore Some kind of positive selection Some element of negative selection

Primary immune response Launch a response to invading pathogens

unspecific response (Leucoytes)

Secondary immune response Remember past encounters (immunologic memory) Faster response the second time around

specific response (B-cells, T-cells)

[SelfOrg] 5.19

Lifecycle of a T-cell

Randomly created

Immature Mature & naive

Cell death (apoptosis)

Activated

Memory / stimulation

No m

atch

dur

ing m

atur

ation

Match during tolerization

Activa

tion

No activation during lifetime

Co-stimulation

No co-stimulation

[SelfOrg] 5.20

Reinforcement Learning and Immune Memory

Repeated exposure to an antigen throughout a lifetime Primary and secondary immune responses Remembers encounters

No need to start from scratch Memory cells

Associative memory

Antigen Ag1 Antigens Ag1, Ag2

Primary Response Secondary Response

Lag

Response to Ag1

Ant

ibod

y C

once

ntra

tion

Time

Lag

Response to Ag2

Response to Ag1

...

...

Cross-Reactive Response

...

...

Antigen Ag1 + Ag3

Response to Ag1 + Ag3

Lag

[SelfOrg] 5.21

Immune Pattern Recognition

The immune recognition is based on the complementarity between the binding region of the receptor and a portion of the antigen called epitope

Antibodies present a single type of receptor, antigens might present several epitopes This means that different antibodies can recognize a single antigen

Antigen 1

Epitopes

Antigen 2

Receptor

Lymphocytes

[SelfOrg] 5.22

Affinity measure

Representation – shape-space Describe the general shape of a molecule Describe interactions between molecules Degree of binding between molecules

[SelfOrg] 5.23

Affinity measure

Real-valued shape-space – the attribute strings are real-valued vectors Integer shape-space – the attribute strings are composed of integer values Hamming shape-space – composed of attribute strings built out of a finite

alphabet of length k Symbolic shape-space – usually composed of different types of attribute

strings where at least one of them is symbolic, such as a ’age’, a ’height’, etc.

Assume the general case in which an antibody molecule is represented by the set of coordinates Ab = Ab1, Ab2, ..., AbL, and an antigen is given by

Ag = Ag1, Ag2, ..., AgL, where boldface letters correspond to a string

[SelfOrg] 5.24

Affinity measure

Affinity is related to distance

Euclidian

Manhatten

Hamming

L

iii AgAbD

1

2)(

L

iii AgAbD

1

otherwise0

if1 ,

1

iii

L

i

iAgAb

D

[SelfOrg] 5.25

AIS – Application Examples

Fault and anomaly detection Data mining (machine learning, pattern recognition) Agent based systems Autonomous control and robotics Scheduling and other optimization problems Security of information systems

[SelfOrg] 5.26

Virus Detection or A Computer Immune System

Protect the computer from unwanted viruses Initial work by Kephart 1994

Detect Anomaly

Scan for known viruses

Capture samples using decoys

Extract Signature(s)

Add signature(s) to databases

Add removal infoto database

Segregatecode/data

AlgorithmicVirus Analysis

Send signals toneighbor machines

Remove Virus

[SelfOrg] 5.27

Forrests Model

Hofmeyr & Forrest (1999, 2000) developed an artificial immune system that is distributed, robust, dynamic, diverse and adaptive, with applications to computer network security

Datapath triple

(20.20.15.7, 31.14.22.87, ftp)

Broadcast LAN

ip: 31.14.22.87port: 2000

Internal host

External host

ip: 20.20.15.7

port: 22

Host

Activationthreshold

Cytokinelevel

Permutationmask

Detectorset

immature memory activated matches

0100111010101000110......101010010

Detector

[SelfOrg] 5.28

Properties Basis of all biological systems Specificity of information transfer Similar structures in biology and in technology especially in computer networking

Concepts Intracellular signaling – Intracellular signaling refers to the information processing

capabilities of a single cell. Received information particles initiate complex signaling cascades that finally lead to the cellular response.

Intercellular signaling – Communication among multiple cells is performed by intercellular signaling pathways. Essentially, the objective is to reach appropriate destinations and to induce a specific effect at this place.

Lessons to learn from biology Efficient response to a request Shortening of information pathways Directing of messages to an applicable destination

Molecular and Cell Biology

Bio-inspired

Networking

Bio-inspired

Networking

[SelfOrg] 5.29

Intracellular Information Exchange

Local: a signal reaches only cells in the neighborhood. The signal induces a signaling cascade in each target cell resulting in a very specific answer which vice versa affects neighboring cells

DNA

Signal (information)

Gene transcription results in the formation of a specific cellular response to the signal

Receptor

[SelfOrg] 5.30

Intercellular Information Exchange

Remote: a signal is released in the blood stream, a medium which carries it to distant cells and induces an answer in these cells which then passes on the information or can activate helper cells (e.g. the immune system)

DNATissue 1

Tissue 2

DNA

DNA

DNA

DNA

DNA

Tissue 3

Blood

[SelfOrg] 5.31

Signaling pathways

Communication with othercells via cell junctions

Nucleus

Neighboring cell

DNA Gene transcription

mRNA translation into proteins

Intracellular signaling molecules

Reception of signaling molecules

Secretion of hormones etc.

Nucleus

DNA

Nucleus

DNA

Reception of signaling molecules (ligands such as hormones, ions, small molecules)

Different cellular answer

(1-a)

(1-b)

(2)

(3-a)

(3-b)

Submission of signaling molecules

Neighboring cell

[SelfOrg] 5.32

Signaling pathways


Nucleus

Neighboring cell






Nucleus

DNA

Nucleus

DNA



(1-a)

(1-b)

(2)

(3-a)

(3-b)


Neighboring cell

(1) Reception of signaling molecules via receptorsCellular signaling cascades are often initiated by the reception of signaling molecules (ligangs) via receptors.

(1-a) Receptors can be expressed on the cell surface. In consequence, ligands bind to cell surface receptors and initiate the activation of a cascade of intracellular molecules. Typical examples are several growth factors.(1-b) Receptors can be expressed as intracellular receptors. In consequence, ligands have to enter the cell to bind the receptor. Examples are effects of steroide hormones such as cortisol.

Additional signaling molecules may affect the established signaling cascade towards the nucleus. The cellular answer is relying on the nucleus to initiate the desired process. In particular, a specific reaction is induced by gene transcription and the translation of mRNA into new proteins.

(1) Reception of signaling molecules via receptorsCellular signaling cascades are often initiated by the reception of signaling molecules (ligangs) via receptors.

(1-a) Receptors can be expressed on the cell surface. In consequence, ligands bind to cell surface receptors and initiate the activation of a cascade of intracellular molecules. Typical examples are several growth factors.(1-b) Receptors can be expressed as intracellular receptors. In consequence, ligands have to enter the cell to bind the receptor. Examples are effects of steroide hormones such as cortisol.

Additional signaling molecules may affect the established signaling cascade towards the nucleus. The cellular answer is relying on the nucleus to initiate the desired process. In particular, a specific reaction is induced by gene transcription and the translation of mRNA into new proteins.

[SelfOrg] 5.33

Signaling pathways


Nucleus

Neighboring cell






Nucleus

DNA

Nucleus

DNA



(1-a)

(1-b)

(2)

(3-a)

(3-b)


Neighboring cell

(2) Indirect stimulation of cellular processes

A signaling molecule can directly enter the cell and is processed in a biochemical reaction. The resulting product changes the behavior or state of the cell. For example, nitric oxide leads to smooth muscle contraction.

(2) Indirect stimulation of cellular processes

A signaling molecule can directly enter the cell and is processed in a biochemical reaction. The resulting product changes the behavior or state of the cell. For example, nitric oxide leads to smooth muscle contraction.

[SelfOrg] 5.34

Signaling pathways


Nucleus

Neighboring cell






Nucleus

DNA

Nucleus

DNA



(1-a)

(1-b)

(2)

(3-a)

(3-b)


Neighboring cell

(3) Cellular answer, e.g. submission of signaling molecules

The cellular answer is a specific response according to the received signaling molecules and the current constitution of the cell. For example, signaling molecules can be created to send messages to other cells.

(3-a) In response to a received information particle a new message can be created and submitted into the extracellular space, e.g. secretion of hormones.(3-b) Additionally, messages can be forwarded to a neighboring cell via a paracellular pathway (via intracellular signaling molecules and a cell-junction), e.g. submission of signaling molecules.

(3) Cellular answer, e.g. submission of signaling molecules

The cellular answer is a specific response according to the received signaling molecules and the current constitution of the cell. For example, signaling molecules can be created to send messages to other cells.

(3-a) In response to a received information particle a new message can be created and submitted into the extracellular space, e.g. secretion of hormones.(3-b) Additionally, messages can be forwarded to a neighboring cell via a paracellular pathway (via intracellular signaling molecules and a cell-junction), e.g. submission of signaling molecules.

[SelfOrg] 5.35

Adaptation to Networking

Local mechanisms Adaptive group formation Optimized task allocation Efficient group communication Data aggregation and filtering Reliability and redundancy

Remote mechanisms Localization of significant relays,

helpers, or cooperation partners Semantics of transmitted messages Cooperation across domains Internetworking of different

technologies Authentication and authorization

[SelfOrg] 5.36

Example: Regulation of Blood Pressure

Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE

KidneyAterial blood pressure ↓

Aterial blood pressure ↑

Increase ofblood volume

Smooth muscle cells: contraction

Kidney: aldosterone à Na+ retention à regulation of blood volume

Adenohypophysis (brain): vasopressin

[SelfOrg] 5.37

Shifting the Paradigm: Feedback Loop Mechanism

Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE

Kidney



Event




[SelfOrg] 5.38


S Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE






Event

[SelfOrg] 5.39


request

S






Event

[SelfOrg] 5.40


The smooth muscle cells, the kidney and the brain team up

one “meta” node This node knows the answer to the request

request

S



Event

S

[SelfOrg] 5.41


No confirmation message is needed The change of the environment indicates the successful initiation of

the task

request

SEvent

S

change of the environment

[SelfOrg] 5.42

Feedback Loop Mechanism

Feedback loop mechanism density of the sensor network allows for alternate feedback loops via the

environment: directly via the physical phenomena which are to be controlled by the infrastructure

indirect communication, allows for more flexible organization of autonomous infrastructures, reduces control messages

Efficient, reliable, robust? one potential benefit lies in a better system efficiency and reliability, explicitly in

unreliable multihop ad-hoc wireless sensor networks we currently implement these techniques in a sensor/robot network and evaluate

them we also develop simulation models (discrete event, stochastic) for larger systems

More concepts from biology can potentially be adopted to allow for adaptive and self-organizing structures more feedback loops: when enough messages for one type of control have entered

the network they throttle the generation of new messages diffuse communication (no addresses, priorities, random dissemination)

[SelfOrg] 5.43

Conclusions

Self-organization in for communication and coordination between networked embedded systems, i.e. in WSN and SANET Many objectives, many directions, similar solutions Bio-inspired networking is just one but powerful approach

[SelfOrg] 5.44

Summary (what do I need to know)

Bio-inspired networking Ideas and objectives

Swarm intelligence Principles – pheromone trails Ant colony optimization – with application in ad hoc routing

Artificial immune system Principles – reinforcement learning Anomaly detection

Cellular signaling pathways Principles – intracellular and intercellular signaling cascades Specific reaction on environmental changes

[SelfOrg] 5.45

References

E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems. New York, Oxford University Press, 1999.

M. Dorigo, V. Maniezzo, and A. Colorni, "The Ant System: Optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, vol. 26 (1), pp. 1-13, 1996.

G. Di Caro and M. Dorgio, "AntNet: Distributed Stigmergetic Control for Communication Networks," Journal of Artificial Intelligence Research, vol. 9, pp. 317-365, December 1998.

G. Di Caro, F. Ducatelle, and L. M. Gambardella, "AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks," European Transactions on Telecommunications, Special Issue on Self-organization in Mobile Networking, vol. 16, pp. 443-455, 2005.

F. Dressler and I. Carreras (Eds.), Advances in Biologically Inspired Information Systems - Models, Methods, and Tools, Studies in Computational Intelligence (SCI), vol. 69. Berlin, Heidelberg, New York, Springer, 2007.

F. Dressler, B. Krüger, G. Fuchs, and R. German, "Self-Organization in Sensor Networks using Bio-Inspired Mechanisms," Proceedings of 18th ACM/GI/ITG International Conference on Architecture of Computing Systems - System Aspects in Organic and Pervasive Computing (ARCS'05): Workshop Self-Organization and Emergence, Innsbruck, Austria, March 2005, pp. 139-144.

S. A. Hofmeyr and S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol. 8 (4), pp. 443-473, 2000.

J. O. Kephart, "A Biologically Inspired Immune System for Computers," Proceedings of 4th International Workshop on Synthesis and Simulation of Living Systems, Cambridge, Massachusetts, USA, 1994, pp. 130-139.

J. Kim and P. J. Bentley, "Towards an Artificial Immune System for Network Intrusion Detection," Proceedings of IEEE Congress on Evolutionary Computation (CEC), Honolulu, May 2002, pp. 1015-1020.

T. H. Labella and F. Dressler, "A Bio-Inspired Architecture for Division of Labour in SANETs," Proceedings of 1st IEEE/ACM International Conference on Bio-Inspired Models of Network, Information and Computing Systems (IEEE/ACM BIONETICS 2006), Cavalese, Italy, December 2006.

Date post:	25-Dec-2015
Category:	Documents
Upload:	doreen-briggs
View:	217 times
Download:	0 times

[SelfOrg]5.1 Self-Organization in Autonomous Sensor/Actuator Networks [SelfOrg] Dr.-Ing. Falko...

Documents